TY - CONF A1 - Heimann, Jan A1 - Charmi, Amir A1 - Karapanagiotis, Christos A1 - Hashemi, Seyedreza A1 - Prager, Jens T1 - How structural health monitoring can be embedded in a digital quality infrastructure: an example. N2 - The digital Quality Infrastructure (QI) initiative “QI-Digital” in Germany is focusing on implementing new technologies and approaches to ensure that the task of quality assurance is more efficient and ready for the digital and green transformation of the economy. The implementation of quality control key elements, such as Smart Standards, Digital Certificates and QI-cloud solutions shall contribute to solving the socio-economic, ecological, and technological challenges of our time. Hydrogen is a key energy carrier and has the potential to play a significant role in the energy transition, especially in mobility. An essential factor for the broad acceptance of hydrogen-based mobility is the availability of refueling stations that operate reliably and safely. Using the example of a Hydrogen Refueling Station (HRS) built within the QI-Digital initiative, the Federal Institute for Material Research and Testing (BAM) aims to establish a real laboratory where modern measurement techniques and new digital methods are implemented to enhance operational safety, availability, and economic efficiency and render the technology more attractive for the industry. In this work, we present an approach to establish a Structural Health Monitoring (SHM) system on a high-pressure buffer inside HRS and show how it could be embedded into a digital QI. The high-pressure buffers are essential components of the plant which are currently inspected periodically without regard to their operating history. Focusing on the transition to a continuous and digitally supported monitoring of the component’s integrity during operation the novel inspection scheme will be linked to a completely digitalized component-related documentation and tested using digital certificates. This allows the operational safety and, if necessary, the remaining useful lifetime to be assessed on an ongoing basis and to be a valuable contribution to increasing sustainability. T2 - 11th European Workshop on Structural Health Monitoring CY - Potsdam, Germany DA - 10.06.2024 KW - Structural health monitoring KW - Quality infrastructure KW - Digital transformation KW - Reliability KW - Quality control KW - Industry 4.0 PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-607109 DO - https://doi.org/10.58286/29769 SN - 1435-4934 SP - 1 EP - 7 PB - NDT.net AN - OPUS4-60710 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Karapanagiotis, Christos T1 - Machine learning for strain, temperature and humidity discrimination in Brillouin optical frequency domain analysis N2 - We propose to our knowledge for the first time an ensemble of probabilistic machine learning models for strain, temperature and humidity discrimination in Brillouin optical frequency domain analysis (BOFDA) applying a two-fiber configuration. T2 - 28th International Conference on Optical Fiber Sensors CY - Hamamatsu, Japan DA - 20.11.2023 KW - Multiparameter sensing KW - Fiber optic sensors KW - Machine learning KW - Structural health monitoring PY - 2023 AN - OPUS4-59396 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Karapanagiotis, Christos ED - Krebber, Katerina T1 - Machine learning for strain, temperature and humidity discrimination in Brillouin optical frequency domain analysis N2 - We propose to our knowledge for the first time an ensemble of probabilistic machine learning models for strain, temperature and humidity discrimination in Brillouin optical frequency domain analysis (BOFDA) applying a two-fiber configuration. T2 - 28th International Conference on Optical Fiber Sensors CY - Hamamatsu, Japan DA - 20.11.2023 KW - Multiparameter sensing KW - Machine learning KW - Fiber optic sensors KW - Structural health monitoring PY - 2023 SP - 1 EP - 4 PB - Optica Publishing Group AN - OPUS4-59397 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Karapanagiotis, Christos T1 - Towards predictive maintenance of hydrogen pressure vessels based on multi-sensor data fusion and digital twin modeling N2 - Hydrogen pressure vessels are among the most essential components for reliable hydrogen technology. Under current regulations, a mostly conservative strategy is employed, restricting the usage time of hydrogen pressure vessels without providing information on the real remaining lifetime. During the service life, pressure vessels are inspected periodically. However, no established method that can provide continuous monitoring or information on the remaining safe service life of the vessel. In this paper, we propose a sensor network for Structural Health Monitoring (SHM) of hydrogen pressure vessels where data from all sensors are collected and centrally evaluated. Specifically, we integrate three different SHM sensing technologies namely Guided Wave ultrasonics (GW), Acoustic Emission testing (AT), and distributed Fiber Optic Sensing (FOS). This integrated approach offers significantly more information and could therefore enable a transition from costly and time-consuming periodic inspections to more efficient and modern predictive maintenance strategies, including Artificial Intelligence (AI)-based evaluation. This does not only have a positive effect on the operational costs but enhances safety through early identification of critical conditions in the overall system in real-time. We demonstrate an experimental set-up of a lifetime test where a Type IV Composite Overwrapped Pressure Vessel (COPV) is investigated under cyclic loading instrumented with AT, FOS, and GW methods. We acquired data from the sensor network until the pressure vessel failed due to material degradation. The data collected using the three different SHM sensor technologies is planned to be evaluated individually, using data fusion, and AI. In the future, we aim to integrate the measurement setup into a hydrogen refueling station with the data stream implemented into a digital signal processing chain and a digital twin. T2 - 11th European Workshop on Structural Health Monitoring CY - Potsdam, Germany DA - 10.06.2024 KW - Hydrogen KW - Ultrasonic guided waves KW - Fiber optic sensors KW - Acoustic emission KW - Machine learning KW - Pressure vessels KW - Structural health monitoring PY - 2024 AN - OPUS4-60277 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Karapanagiotis, Christos T1 - Structural health monitoring of hydrogen pressure vessels using distributed fiber optic sensing N2 - We report on distributed fiber optic sensing-based monitoring of hydrogen composite overwrapped pressure vessels (COPV) to simultaneously increase the operational lifespan and mitigate maintenance costs. Our approach represents, to the best of our knowledge, the first application of distributed fiber optic sensing for COPV Type IV monitoring, where the sensing fibers are attached to the surface, rather than integrated into the composite material. Specifically, we attach an optical fiber of 50 m to the pressure vessel's surface, covering both the cylindrical and dome sections. We note that our fiber optic sensing technique relies on swept wavelength interferometry providing strain information along the entire length of the optical fiber with high spatial resolution even at the millimeter scale. When the vessel is pressurized, the sensing optical fiber shows a linear strain response to pressure at every position along the fiber. After thousands of load cycles, the vessel finally fails with the optical fiber detecting and precisely localizing the damage in the vessel’s blind dome area. Furthermore, we discuss the potential of state-of-the-art signal processing methods and machine learning for advancing predictive maintenance. This could reduce the number of regular inspections, mitigate premature maintenance costs, and simultaneously increase the vessel’s remaining safe service life. We believe that the structural health monitoring of hydrogen pressure vessels with fiber optic sensors can enhance trust in hydrogen technology contributing to the energy transition in the future. T2 - 11th European Workshop on Structural Health Monitoring CY - Potsdam, Germany DA - 10.06.2024 KW - Hydrogen KW - Composites KW - Pressure vessels KW - Fiber optic sensors KW - Machine learning KW - Structural health monitoring PY - 2024 AN - OPUS4-60275 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Karapanagiotis, Christos A1 - Schukar, Marcus A1 - Breithaupt, Mathias A1 - Krebber, Katerina T1 - Monitoring of composite pressure vessels using surface applied distributed fiber optic sensors N2 - In this paper, we report on surface-applied distributed fibre optic sensors for monitoring composite pressure vessels designed for hydrogen storage. Previous reports have revealed that integrating optical fibres within vessel composite structures effectively enables the monitoring of structural behavior throughout their lifetime. However, integrating optical fibres during the manufacturing process is complex and time-consuming. Therefore, we aim to simplify this process by attaching the optical fibres to the vessel’s surface. This method is significantly more timeefficient than the integration process and can be applied to any vessel. Our results demonstrate that surface-applied fibre optic sensors can detect and precisely localise damage. Additionally, signs of damage can be recognised even before the damage occurs. Predictive maintenance using fibre optic sensors could reduce premature maintenance costs and periodic inspections while increasing safety and extending the vessel’s useful service life. The role of machine learning in predictive maintenance is also discussed. T2 - 20th Sensors & their Applications Conference CY - Limerick, Ireland DA - 11.08.2024 KW - Hydrogen KW - Fiber optic sensors KW - Composites KW - Machine learning KW - Structural health monitoring PY - 2024 SP - 1 EP - 4 AN - OPUS4-60911 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Karapanagiotis, Christos T1 - Monitoring hydrogen composite pressure vessels using surface applied distributed fiber optic sensors N2 - We report on surface-applied distributed fibre optic sensors for monitoring composite pressure vessels designed for hydrogen storage. Previous reports have revealed that integrating optical fibres within vessel composite structures effectively enables the monitoring of structural behavior throughout their lifetime. However, integrating optical fibres during the manufacturing process is complex and time-consuming. Therefore, we aim to simplify this process by attaching the optical fibres to the vessel’s surface. This method is significantly more timeefficient than the integration process and can be applied to any vessel. Our results demonstrate that surface-applied fibre optic sensors can detect and precisely localise damage. Additionally, signs of damage can be recognised even before the damage occurs. Predictive maintenance using fibre optic sensors could reduce premature maintenance costs and periodic inspections while increasing safety and extending the vessel’s useful service life. The role of machine learning in predictive maintenance is also discussed. T2 - 20th Sensors & their Applications Conference CY - Limerick, Ireland DA - 11.08.2024 KW - Hydrogen KW - Fiber optic sensors KW - Composites KW - Machine learning KW - Structural health monitoring PY - 2024 AN - OPUS4-60831 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Karapanagiotis, Christos T1 - Toward a Digital Twin of Hydrogen Pressure Vessels Enabled by Distributed Fiber Optic Sensors N2 - We present a digital replica of a hydrogen pressure vessel enabled by distributed fiber optic sensors (DFOS). This digital replica dynamically displays and updates the vessel’s structural condition by calculating strain residuals defined as the difference between the measured DFOS strain and the expected strain based on pressure data. As an example, we show the ability of the DFOS to detect and localize damage caused by drilling six holes into the vessel’s body. This digital replica represents a foundational step toward a fully integrated digital twin for predictive maintenance and remaining lifetime prognosis. T2 - Sensor and Measurement Science International 2025 CY - Nuremberg, Germany DA - 06.05.2025 KW - Fiber optic sensors KW - Hydrogen KW - Digital twins KW - Structural health monitoring KW - Machine learning KW - Predictive maintenance PY - 2025 AN - OPUS4-63087 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - JOUR A1 - Karapanagiotis, Christos A1 - Heimann, Jan A1 - Duffner, Eric A1 - Charmi, Amir A1 - Schukar, Marcus A1 - Hashemi, Seyedreza A1 - Prager, Jens T1 - Towards predictive maintenance of hydrogen pressure vessels based on multi-sensor data N2 - In this paper, we report on a sensor network for structural health monitoring (SHM) of Type IV composite overwrapped pressure vessels (COPVs) designed for hydrogen storage. The sensor network consists of three different SHM sensing technologies: ultrasonic guided waves (GW), acoustic emission (AE) testing, and distributed fiber optic sensors (DFOS). We present an experimental setup for a lifetime test, where a COPV is subjected to cyclic loading. Data from all sensors are collected and centrally evaluated. The COPV failed after approximately 60,000 load cycles, and the sensor network proved capable of detecting and localizing the damage even before the failure of the COPV. This multi-sensor approach offers significantly more channels of information and could therefore enable a transition from costly and time-consuming periodic inspections to more efficient and modern predictive maintenance strategies, including artificial intelligence (AI)-based evaluation. This not only has a positive effect on operational c KW - Ffiber optic sensors KW - Acoustic emission KW - Guided waves KW - Hydrogen KW - Digital twin KW - Structural health monitoring PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-618948 DO - https://doi.org/10.58286/30513 SP - 1 EP - 8 PB - NDT.net AN - OPUS4-61894 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER - TY - CONF A1 - Karapanagiotis, Christos A1 - Heimann, Jan A1 - Duffner, Eric A1 - Charmi, Amir A1 - Schukar, Marcus A1 - Hashemi, Seyedreza A1 - Prager, Jens T1 - Towards predictive maintenance of hydrogen pressure vessels based on multi-sensor data fusion and digital twin modeling N2 - Hydrogen pressure vessels are among the most essential components for reliable hydrogen technology. Under current regulations, a mostly conservative strategy is employed, restricting the usage time of hydrogen pressure vessels without providing information on the real remaining lifetime. During the service life, pressure vessels are inspected periodically. However, no established method that can provide continuous monitoring or information on the remaining safe service life of the vessel. In this paper, we propose a sensor network for Structural Health Monitoring (SHM) of hydrogen pressure vessels where data from all sensors are collected and centrally evaluated. Specifically, we integrate three different SHM sensing technologies namely Guided Wave ultrasonics (GW), Acoustic Emission testing (AT), and distributed Fiber Optic Sensing (FOS). This integrated approach offers significantly more information and could therefore enable a transition from costly and time-consuming periodic inspections to more efficient and modern predictive maintenance strategies, including Artificial Intelligence (AI)-based evaluation. This does not only have a positive effect on the operational costs but enhances safety through early identification of critical conditions in the overall system in real-time. We demonstrate an experimental set-up of a lifetime test where a Type IV Composite Overwrapped Pressure Vessel (COPV) is investigated under cyclic loading instrumented with AT, FOS, and GW methods. We acquired data from the sensor network until the pressure vessel failed due to material degradation. The data collected using the three different SHM sensor technologies is planned to be evaluated individually, using data fusion, and AI. In the future, we aim to integrate the measurement setup into a hydrogen refueling station with the data stream implemented into a digital signal processing chain and a digital twin. T2 - 11th European Workshop on Structural Health Monitoring CY - Potsdam, Germany DA - 10.06.2024 KW - Acoustic emission KW - Ultrasonic guided waves KW - Fiber optic sensors KW - Hydrogen KW - Pressure vessels KW - Structural health monitoring KW - Machine learning PY - 2024 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-602764 UR - https://www.ndt.net/search/docs.php3?id=29702 SP - 1 EP - 8 PB - NDT.net AN - OPUS4-60276 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -